This paper presents a novel power estimation method for large and complex LSIs. The proposed method is based on simulation and is used for analyzing the ways in chip-scale gate-level circuits including processors and memory are affected by gated-clock power reduction and the voltage drop due to electrical resistance. The chip-scale power estimation based on simulation patterns generally takes enormous time. In order to reduce the time to obtain accurate estimation results based on simulation patterns, we introduce three approaches: "partitioning of target LSIs and simulation pattern," "memory modeling," and "processor modeling." After placing and routing, the target LSIs are partitioned into hierarchical blocks, memory, and processors. The power consumption of each hierarchical block is calculated by using the partitioned patterns generated from chip-scale simulation patterns. The power consumption of the processor and memory blocks is estimated by a method considering the static power consumption and the rate of LSI activity ratio. Experimental results for a commercial 0.18 µm-technology media processing chip show that the proposed method is 23 times faster than the conventional method without partitioning and that both the results are almost the same.

In the final stages of VLSI testing, improved quality VLSI testing is an important subject for ensuring reliability in the forwarded VLSI market. On the other hand, developments in high integration technology have resulted in an increased number of functional blocks in VLSI devices and an increased number of gates for each terminal. Consequently, it has become more difficult to improve the quality of VLSI tests. We have developed a new test method in addition to conventional testing methods intended for improving the test coverage in VLSI tests. This new test method analyzes the relationship between IDDq (Quiescent Power Supply Current) of DUT and DUT failure by applying the concept of the toggle rate. Accordingly, in this paper we report that the results of IDDq testing confirm a correlation with defect level.